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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Athanasios Papadimitriou, Orestis Al-Hussaeni, Khalil Karamitsos, Ioannis Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates Rochester Institute of Technology Graduate and Research Department Dubai United Arab Emirates Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350362480

Image classification is a fundamental task in computer vision, with wide applications including autonomous navigation, content recommendation, and environmental monitoring. This paper offers a comprehensive comparative analysis of deep learning techniques using the Intel Natural Scenes Image dataset, which features diverse scenes such as forests, mountains, seas, streets, buildings, and glaciers. Our study evaluates the performance of various Convolutional Neural Network (CNN) architectures, focusing on aspects such as network design, hyperparameters, data augmentation, and transfer learning strategies. We describe our experimental setup in detail, including the CNN models used, preprocessing techniques applied, and evaluation metrics employed. The results and discussions present key findings, highlighting the strengths and limitations of the approaches studied and providing guidance for future research and practical applications. Our systematic analysis yields valuable insights into effective strategies for recognizing natural scenes. © 2024 IEEE.

关键词： Convolutional neural networks

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Innovative Byzantine Fault Tolerance in Hyperledger Fabric with BDLS

Innovative Byzantine Fault Tolerance in Hyperledger Fabric w...

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Blockchain computing and Applications (BCCA), International Conference on

作者： Ahmed Al Salih Yongge Wang Software and Information Systems Department College of Computing and Informatics University of North Carolina at Charlotte Charlotte NC USA Software and Information Systems Department (SIS) College of Computing and Informatics University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

Decentralized systems are integral to various sectors, including public and private organizations. A key component of these systems is the dissemination protocol. Hyperledger Fabric, a prominent production-ready distribution network, allows for a pluggable consensus protocol, but has historically lacked detailed technical guidance for implementing such modules. Initially, Fabric employed Kafka and later switched to Raft, both Crash Fault Tolerant (CFT) protocols that do not support Byzantine Fault Tolerance (BFT). Recently, Hyperledger Fabric introduced SmartBFT in its 3.0 release, a new BFT ***, SmartBFT, based on the PBFT family, suffers from high message complexity that scales poorly with network growth, undermining efficiency for large-scale implementations in industries such as healthcare, finance, and education. Our research demonstrates that the message complexity of SmartBFT grows quadratically with the number of orderer nodes, leading to significant communication overhead. In contrast, our proposed implementation of BDLS maintains linear message complexity, making it more scalable and efficient for large networks. This stark contrast highlights BDLS’s superior scalability and efficiency. Additionally, BDLS consistently outperforms SmartBFT in throughput analysis of Fabric 3.0, achieving significantly higher TPS—40% and 20% of Raft’s TPS in LAN and WAN environments, respectively. All code is available in the GitHub repository: https://***/hyperledger-labs/bdls.

关键词： Industries Wide area networks Fault tolerance Distributed ledger Scalability Fault tolerant systems Switches Throughput Fabrics Complexity theory

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Enabling data-driven anomaly detection by design in cyber-physical production systems

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Cybersecurity 2022年第3期5卷 34-51页

作者： Rui Pinto Gil Goncalves Jerker Delsing Eduardo Tovar Department of Informatics Engineering Faculty of EngineeringUniversity of PortoPortoPortugal EISLAB Lulea University of TechnologyLuleaSweden Research Centre in Real-Time and Embedded Computing Systems Polytechnic of Porto-School of EngineeringPortoPortugal

Designing and developing distributed cyber-physical production systems(CPPS)is a time-consuming,complex,and error-prone *** systems are typically heterogeneous,i.e.,they consist of multiple components implemented with different languages and development *** of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system’s *** the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design,deployment,and management of CPPS in a model-based engineering *** propose a method for CPPS design based on the IEC 61499 *** method allows designers to embed a bio-inspired anomaly-based host intrusion detection system(A-HIDS)in Edge *** A-HIDS is based on the incremental Dendritic Cell Algorithm(iDCA)and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS’Edge *** study’s findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions,and cost-effective security by design system *** to the experimental data,the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices.

关键词： Artificial immune systems Cyber-physical production systems IEC 61499 Function blocks Industrial Internet of Things Anomaly-based host intrusion detection system Model-based engineering

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Global Graph Clustering and Local Graph Exploration for Community Detection in Twitter 7

Global Graph Clustering and Local Graph Exploration for Comm...

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7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Karras, Christos Karras, Aristeidis Giannoukou, Ioanna Giotopoulos, Konstantinos C. Tsolis, Dimitrios Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Greece University of Patras Department of Management Science and Technology Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In this paper, the concepts and techniques for global graph clustering are examined, or the process of locating related clusters of vertices within a graph. We introduce the construction of a graph clustering technique based on an eigenvector embedding and a local graph clustering method based on stochastic exploration of the graph. Then, the developed implementations of both methods are presented and assessed in terms of performance. In addition, the difficulties associated with assessing clusterings and benchmarking cluster algorithms are explored where PageRank and EigEmbed algorithms are utilized. The experiments show that the EigEmbed outperformed PageRank across all experiments as it detected more communities with the same number of clusters. Ultimately, we apply both algorithms to a real-world graph representing Twitter network and the followers and tweets therein. © 2022 IEEE.

关键词： Random processes

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An Intelligent Microprocessor Integrating TinyML in Smart Hotels for Rapid Accident Prevention 7

An Intelligent Microprocessor Integrating TinyML in Smart Ho...

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7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Zacharia, Angelos Zacharia, Dimitris Karras, Aristeidis Karras, Christos Giannoukou, Ioanna Giotopoulos, Konstantinos C. Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Greece University of Patras Department of Management Science and Technology Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for intelligent microcontrollers that can collect, sense and analyse data effectively and efficiently. Such devices can be installed in large scale IoT deployments ranging from smart homes to smart cities and smart buildings. The aim of these devices shall be not only data monitoring but at the same time energy saving and overall building management. In the context of this paper, an all-in-one microprocessor is presented, namely ZAC888DP, which can sense data from multivariate sources and perform data analytics on top of the collected data. Moreover, machine learning (ML) models are deployed in the embedded memory of the device and specifically TinyML methods using a tflite file. The aim of the developed ML model is to collect data from four heterogeneous sources (water sensor, light sensor, humidity and temperature) in order to identify and forecast possible lavatory accidents. The experimental results of this work are encouraging as the model managed to achieve 100 percentage accuracy after 256 iterations. Future directions include the integration of the device with a neural network that will be trained on top of the pre-trained model in order to increase the overall precision even further. © 2022 IEEE.

关键词： Internet of things

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Feature Fool Exploitation for Lightweight Anomaly Detection in Respiratory Sound 10th

Feature Fool Exploitation for Lightweight Anomaly Detection...

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10th International Conference on Future Data and Security Engineering, FDSE 2023

作者： Le, Kim-Ngoc T. Bang, Sammy Yap Xiang Le, Duc-Tai Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819982950

Respiratory sound auscultation with digital stethoscopes is a common technique for identifying lung disorders, however, it requires a qualified medical expert to interpret the sounds, and inter-listener variability results from subjectivity in interpretations. To improve diagnostic accuracy and enhance patient treatment, there is a growing need for automated detection of lung diseases. Deep neural networks (DNNs) have demonstrated substantial potential in addressing such challenges. However, DNNs demand a significant amount of data, and the largest available respiratory dataset, ICBHI, comprises only 6898 breathing cycles, which is insufficient to train a satisfactory DNN model. To address the issue, we propose a robust and lightweight model that employs a feature fool exploitation technique to identify respiratory anomalies. Next, we deploy two evaluation approaches to evaluate its performance: random 60/40 splitting and 5-fold cross-validation, against state-of-the-art methods, using the ICBHI dataset. Remarkably, our scheme outperforms existing approaches up to 18.26%, achieving impressive accuracy rates of 72.36% and 89.46%, using the two respective train/test splitting methods. The results show a significant improvement from our method over existing approaches, suggesting its promise for future respiratory healthcare technology research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Anomaly detection

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Peer to Peer Federated Learning: Towards Decentralized Machine Learning on Edge Devices 7

Peer to Peer Federated Learning: Towards Decentralized Machi...

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7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Karras, Aristeidis Karras, Christos Giotopoulos, Konstantinos C. Tsolis, Dimitrios Oikonomou, Konstantinos Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece Ionian University Department of Informatics Corfu Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Federated Learning (FL) is an emerging technique that assures user privacy and data integrity in distributed machine learning environments. To perform so, chunks of data are trained across edge devices and a high performance cluster server maintains a local copy without exchanging it with other parties. In this work, we investigate a FL scenario in a real-world case study using 5, 10 and 20 Raspberry Pi devices acting as clients. Under this setup, we employ the widely known FedAvg algorithm which trains each client for several local epochs and then the weight of each model is aggregated. Moreover we perform experiments across imbalanced and noisy data so as to explore scalability and robustness based on real-world datasets were noise is present and we also propose two innovative algorithms where the FL scenario is considered as a peer-to-peer formulation. Ultimately, to ensure that each device is not oversampled a client-balancing Dirichlet sampling algorithm with probabilistic guarantees is proposed. © 2022 IEEE.

关键词： Computer aided instruction

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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